Multiple just-noticeable-difference-based no-reference stereoscopic image quality assessment

Abstract

Just-noticeable difference (JND) is an important characteristic of the human visual system (HVS), and some established JND models imitating the perception of human eyes already exist. However, their utilization in stereoscopic image quality assessment (SIQA) remains limited. To better simulate how HVS senses 3D images under a no-reference situation, a novel SIQA method based on multiple JND models is proposed in this paper. In our metric, the stereoscopic image pairs are decomposed into multi-scale monocular views and binocular views. Then, texture and edge information of these multi-scale images is extracted. Next, a monocular JND model, a binocular JND model, and a depth JND model are separately applied to the extracted features and the depth map. Finally, these features are synthesized and mapped to objective scores. Through experiment and comparison on public 3D image databases, the proposed method shows a competitive advantage over most state-of-the-art SIQA methods, which indicates that it has a promising prospect in practical applications.